Default feature selection in credit risk modeling: Evidence from Chinese small enterprises

Nana Chai, Baofeng Shi*, Bin Meng, Yizhe Dong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper aims to design a novel AFCM-SMOTENC-APRIORI model to mine the default feature attributes of small enterprises. It can overcome the problem that the data characteristics of “small defaulting small enterprises and large non-defaulting small enterprises” make it difficult to mine the defaulting feature attributes of existing small enterprises. We used 1,231 small enterprise credit data from a city commercial bank in China to make an empirical analysis. We found that 23 feature attributes are strongly associated with default and 87% of the association rules are the same between the extended data and the original data mining. It shows that the data mining results with SMOTE-NC are highly consistent with the results of the original data mining, and the model is robust and reliable. It can be used as a reference for the credit risk identification of small enterprises in commercial banks.

Original languageEnglish
JournalSAGE Open
Issue number2
Publication statusPublished - 11 Apr 2023

Keywords / Materials (for Non-textual outputs)

  • association rule
  • feature attributes
  • imbalanced data
  • small enterprise


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